Functional and spatial rewiring jointly generate convergent-divergent units in self-organizing networks

TL;DR

This paper demonstrates how the joint influence of functional and spatial rewiring in neural networks leads to the emergence of convergent-divergent units, which are crucial for context-sensitive information processing.

Contribution

It introduces a neural network model that integrates spatial and functional rewiring, revealing their combined role in forming convergent-divergent units with computational significance.

Findings

Wiring cost minimization supports the formation of convergent-divergent units.

Spatial organization influences the encapsulation of computational cores.

The model shows the emergence of context-sensitive processing units.

Abstract

Self-organization through adaptive rewiring of random neural networks generates brain-like topologies comprising modular small-world structures with rich club effects, merely as the product of optimizing the network topology. In the nervous system, spatial organization is optimized no less by rewiring, through minimizing wiring distance and maximizing spatially aligned wiring layouts. We show that such spatial organization principles interact constructively with adaptive rewiring, contributing to establish the networks' connectedness and modular structures. We use an evolving neural network model with weighted and directed connections, in which neural traffic flow is based on consensus and advection dynamics, to show that wiring cost minimization supports adaptive rewiring in creating convergent-divergent unit structures. Convergent-divergent units consist of a convergent input-hub, connected to a divergent output-hub via subnetworks of intermediate nodes, which may function as the computational core of the unit. The prominence of minimizing wiring distance in the dynamic evolution of the network determines the extent to which the core is encapsulated from the rest of the network, i.e., the context-sensitivity of its computations. This corresponds to the central role convergent-divergent units play in establishing context-sensitivity in neuronal information processing.